Deploy to Databricks Apps

App Naming Convention

Unless the user specifies a different name, apps should use the prefix

agent-*

```
agent-data-analyst
```
```
agent-customer-support
```
```
agent-code-helper
```

Update the app name in

databricks.yml

yaml

resources:
  apps:
    agent_langgraph_long_term_memory:
      name: "agent-your-app-name"  # Use agent-* prefix

Deploy Commands

IMPORTANT: Always run BOTH commands to deploy and start your app:

bash

# 1. Validate bundle configuration (catches errors before deploy)
databricks bundle validate

# 2. Deploy the bundle (creates/updates resources, uploads files)
databricks bundle deploy

# 3. Run the app (starts/restarts with uploaded source code) - REQUIRED!
databricks bundle run agent_langgraph_long_term_memory

Note:
bundle deploy
only uploads files and configures resources.
bundle run
is required to actually start/restart the app with the new code. If you only run
deploy
, the app will continue running old code!

The resource key

agent_langgraph_long_term_memory

matches the app name in

databricks.yml

under

resources.apps

Handling "App Already Exists" Error

databricks bundle deploy

fails with:

Error: failed to create app
Failed to create app <app-name>. An app with the same name already exists.

Ask the user: "Would you like to bind the existing app to this bundle, or delete it and create a new one?"

Option 1: Bind Existing App (Recommended)

Step 1: Get the existing app's full configuration:

bash

# Get app config including budget_policy_id and other server-side settings
databricks apps get <existing-app-name> --output json | jq '{name, budget_policy_id, description}'

Step 2: Update

databricks.yml

to match the existing app's configuration exactly:

yaml

resources:
  apps:
    agent_langgraph_long_term_memory:
      name: "existing-app-name"  # Must match exactly
      budget_policy_id: "xxx-xxx-xxx"  # Copy from step 1 if present

Why this matters: Existing apps may have server-side configuration (like
budget_policy_id
) that isn't in your bundle. If these don't match, Terraform will fail with "Provider produced inconsistent result after apply". Always sync the app's current config to
databricks.yml
before binding.

Step 3: If deploying to a

mode: production

target, set

workspace.root_path

yaml

targets:
  prod:
    mode: production
    workspace:
      root_path: /Workspace/Users/${workspace.current_user.userName}/.bundle/${bundle.name}/${bundle.target}

Why this matters: Production mode requires an explicit root path to ensure only one copy of the bundle is deployed. Without this, the deploy will fail with a recommendation to set
workspace.root_path
.

Step 4: Check if already bound, then bind if needed:

bash

# Check if resource is already managed by this bundle
databricks bundle summary --output json | jq '.resources.apps'

# If the app appears in the summary, skip binding and go to Step 5
# If NOT in summary, bind the resource:
databricks bundle deployment bind agent_langgraph_long_term_memory <existing-app-name> --auto-approve

Note: If bind fails with "Resource already managed by Terraform", the app is already bound to this bundle. Skip to Step 5 and deploy directly.

Step 5: Deploy:

bash

databricks bundle deploy
databricks bundle run agent_langgraph_long_term_memory

Option 2: Delete and Recreate

bash

databricks apps delete <app-name>
databricks bundle deploy

Warning: This permanently deletes the app's URL, OAuth credentials, and service principal.

Unbinding an App

To remove the link between bundle and deployed app:

bash

databricks bundle deployment unbind agent_langgraph_long_term_memory

Use when:

Switching to a different app
Letting bundle create a new app
Switching between deployed instances

Note: Unbinding doesn't delete the deployed app.

Query Deployed App

IMPORTANT: Databricks Apps are only queryable via OAuth token. You cannot use a Personal Access Token (PAT) to query your agent. Attempting to use a PAT will result in a 302 redirect error.

Get OAuth token:

bash

databricks auth token | jq -r '.access_token'

Send request:

bash

curl -X POST <app-url>/invocations \
  -H "Authorization: Bearer <oauth-token>" \
  -H "Content-Type: application/json" \
  -d '{ "input": [{ "role": "user", "content": "hi" }], "stream": true }'

If using memory - include

user_id

to scope memories per user:

bash

curl -X POST <app-url>/invocations \
  -H "Authorization: Bearer <oauth-token>" \
  -H "Content-Type: application/json" \
  -d '{
      "input": [{"role": "user", "content": "What do you remember about me?"}],
      "custom_inputs": {"user_id": "user@example.com"}
  }'

On-Behalf-Of (OBO) User Authentication

To authenticate as the requesting user instead of the app service principal:

python

from agent_server.utils import get_user_workspace_client

# In your agent code
user_client = get_user_workspace_client()
# Use user_client for operations that should run as the user

This is useful when you want the agent to access resources with the user's permissions rather than the app's service principal permissions.

See: OBO authentication documentation

Debug Deployed Apps

bash

# View logs (follow mode)
databricks apps logs <app-name> --follow

# Check app status
databricks apps get <app-name> --output json | jq '{app_status, compute_status}'

# Get app URL
databricks apps get <app-name> --output json | jq -r '.url'

Important Notes

App naming convention: App names must be prefixed with
```
agent-
```
(e.g.,
```
agent-my-assistant
```
,
```
agent-data-analyst
```
)
Name is immutable: Changing the
```
name
```
field in
```
databricks.yml
```
forces app replacement (destroy + create)
Remote Terraform state: Databricks stores state remotely; same app detected across directories
Review the plan: Look for
```
# forces replacement
```
in Terraform output before confirming

FAQ

Q: I see a 200 OK in the logs, but get an error in the actual stream. What's going on?

This is expected behavior. The initial 200 OK confirms stream setup was successful. Errors that occur during streaming don't affect the initial HTTP status code. Check the stream content for the actual error message.

Q: When querying my agent, I get a 302 redirect error. What's wrong?

You're likely using a Personal Access Token (PAT). Databricks Apps only support OAuth tokens. Generate one with:

bash

databricks auth token

Q: How do I add dependencies to my agent?

Use

uv add

bash

uv add <package_name>
# Example: uv add "mlflow-skinny[databricks]"

Troubleshooting

Issue	Solution
Validation errors	Run `databricks bundle validate` to see detailed errors before deploying
Permission errors at runtime	Grant resources in `databricks.yml` (see add-tools skill)
Lakebase access errors	See lakebase-setup skill for permissions (if using memory)
App not starting	Check `databricks apps logs <app-name>`
Auth token expired	Run `databricks auth token` again
302 redirect error	Use OAuth token, not PAT
"Provider produced inconsistent result"	Sync app config to `databricks.yml`
"should set workspace.root_path"	Add `root_path` to production target
App running old code after deploy	Run `databricks bundle run agent_langgraph_long_term_memory` after deploy
Env var is None in deployed app	Check `valueFrom` in app.yaml matches resource `name` in databricks.yml

deploy

NPX Install

Tags

SKILL.md Content

Deploy to Databricks Apps

App Naming Convention

Deploy Commands

Handling "App Already Exists" Error

Option 1: Bind Existing App (Recommended)

Option 2: Delete and Recreate

Unbinding an App

Query Deployed App

On-Behalf-Of (OBO) User Authentication

Debug Deployed Apps

Important Notes

FAQ

Troubleshooting